In this review work we developed a novel algorithm that can generate a narrative report of cricketing domain based on statistical data information extracted from raw values of match data. The algorithm is successful able to match quality standards of creating reporting with fair amount of increase in information gain.

1. International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064
Volume 2 Issue 9, September 2013
www.ijsr.net
Narrative Science: A Review
Ramandeep Ghuman1
, Ripmi Kumari2
1, 2
Swami Vivekanand Institute of Engineering and Technology, Banur, Punjab, India
Abstract: In this review work we developed a novel algorithm that can generate a narrative report of cricketing domain based on
statistical data information extracted from raw values of match data. The algorithm is successful able to match quality standards of
creating reporting with fair amount of increase in information gain.
Keywords: Natural language processing, Narratives science, Data, Information gain.
1. Introduction
Narrative science is branch of NLP which turns data into
stories. Narrative is defined as some kind of retelling often in
words (though it is possible to mime a story) of something
that happened. Narrative meaning is about connections. It
links individuals’ human actions and events into interrelated
aspects of an understandable composite. Narrative science [1]
is a kind of a tech solution that creates rich narrative content
from data. Narrative is seamlessly created from structured
data sources and can be fully customized to fit a customer’s
voice, style and tone. stories are created in multiple formats,
including long form stories ,headlines, tweets and industry
reports with graphical visualizations. Multiple versions of the
same story can be created to customize the content for each
audience’s specific needs. In Narrative paradigm theory the
all meaningful communication is a form of storytelling or
giving a report of events and so human beings experience and
comprehend life as a series of ongoing narratives, each with
their own conflicts, character, beginnings, middles and ends.
With help of narratives all forms of communication that
appeal to our reason can best viewed as stories shaped by
history culture and character and all forms of human
communication are to be seen fundamentally as stories. The
aim of narrative is to provide content and insight in those
areas where it is either financially or logistically impossible
for organizations to generate it themselves using traditional
method. The figure below show how narrative science
converts complex data into story.
 In this first step, narrative science imports our data and
builds an appropriate narrative structure to meet the goals
of our audience.
 In this second step create story using complex Artificial
Intelligence algorithms..It extracts and organized key
facts and insights and transformed them into stories into
stories at scale.
 In this step deliver insight, narrative science uses data to
answer important questions, provide advice and deliver
powerful insight in a precise clear narrative.
1.2 Advantages of Narrative Science
Narrative provides the researcher with an understanding of
data. Narrative gives the researcher access to stories or
themes that the story teller may not even be conscious of
Narrative highlights changing perspective and understanding
of people and events as a function of time in the evaluation
of an experience. Narrative science can help tackle a wide
variety of business challenges and a broad range of company
types. e.g Forbes.com uses narrative’s platform to generate
corporate earnings preview stories. Narrative science helps
companies leverage their data by automatically creating easy
to use and consistent narrative reporting through our patented
artificial intelligence platform. Narrative science turns big
data into plain English and making sense of data. Big Data is
defined as “data sets” whose size is beyond the ability of
commonly used software tools to capture, manage and
process the data within a tolerable elapsed time. Big Data is
made of structured and unstructured information.10%
structured information is the data in databases and is about
10% of story. Unstructured information is 90% of big data
and is human information like emails, videos, tweets,
facebook posts, call-center conversations, closed circuit
television footage, mobile calls, website etc. Work with
narrative science is easy to understand. With the help of
narrative science we have more insights means deep
knowledge in local language. Narrative science is more
understanding and culture customized.
1.3 Applications or Why We Use Narrative Science
We use narrative science to solve the problem of information
explosion. People are essentially storytellers. Making
decisions depends on judgments about ‘good reasons’. We
are generating stories in the arenas of sports, finance, real
estate and politics when people experience a story, the phase
of comprehension is where people form a mental
representation about the text. The mental representation that
is formed is called a situation model while narrative science
began its life providing content for Media Company
Paper ID: 12013162 205

2. International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064
Volume 2 Issue 9, September 2013
www.ijsr.net
2. Methodology of Work
Dataset of particular topic-In this we can take dataset of
sports data like cricket match. The column of the table
represents matches and each row corresponds to attribute like
date of match, day, place, teams, bowling teams, batting
teams etc of given matches. Each value is known as a datum.
Extract statistical information-In this step we can extract
statistical information or we can say measure data in the
arithmetic mean which is an average value for a group of
numerical observation.
Develop formula base algorithm-From extract data or
information we can develop formula based algorithm. This
algorithm is rule base algorithm in which our said event
recorded in dataset.
Run story based on dataset-In this step narration story is
given out as output..Story is based on the dataset. Algorithm
extracts the key facts and interesting insights from the data
and transforms them into stories.
Calculate Information gain-Information gain is the
expected reduction in entropy caused by partitioning the
examples according to a given attributes. Entropy comes
from information gain, higher thentropy the more the
information content. Entropy is a common way to measure
impurity. Entropy is very common in information theory
characterizes the (im) purity of an arbitrary collection of
examples.
3. Results
Figure 1: Information gain
Entropy defines the purity of Information of an arbitrary
collection of examples.
Information Gain is the expected reduction in entropy
earned by partitioning the examples according to a given
attribute.
Figure 2: Information gain with narrative coefficient
Figure 3: Information gain value before adding narration
and after adding narration
Narrative Coefficient =Relevant Narrative Score⁄ Max.
Narrative Score
Figure 4 Information gain value before adding narration and
after adding narration
Relavance Narration Score = Intro ɴ+Bodyɴ+Concɴ+Tittle ɴ
Paper ID: 12013162 206

3. International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064
Volume 2 Issue 9, September 2013
www.ijsr.net
Figure 5: Find the information gain ratio
Information Gain Ratio = Information gain/Intrinsic
value=IG/Iv
4. Conclusion
All the results show that there is many folds of information
gain when narration is added to dataset which are already
based on the probability of events ,most of the reports,
journals, articles, today’s seems to have ‘formula’ or ‘plot’ in
their expression ,these ‘formula’ or ‘plots’ can also be
understood in terms of their mathematical existence,
especially when data set is analyzed on the basis of
Descriptive statistics in which mean , average ,frequency,
mode min and max values can be translated into meaningful
,along with its semantics narration which would be of
machine quality but would be score fairly high in terms of
qualitative perception of humans. By enhancing the
information theory which is expressed in terms of probability
now with narration of events captured in dataset, we were able
to generate automatic report tool which generates the report in
HTML format.
References
[1] Narrative Science, http://www.narrativescience.com
(accessed in August 2012).
[2] Lemke, J. L. (1990). Talking Science: Language,
learning and Values. Norwood, NJ: Ablex Publishing.
[3] Abell P (1987). The Syntax of Social Life: The Theory
and Method of Comparative Narratives, New York,
Oxford University Press.
[4] Hempel CG (1965). Aspects of Scientific Explanation
and Other Essaysin the Philosophy of Science, New
York, Free Press.
[5] Lowe NJ (2004). The Classical Plot and the Invention
of Western Narrative, Cambridge University Press.
[6] Polkinhorne DE (1988). Narrative knowing and the
human sciences.New York: State University of New
York Press.
[7] Negrete A, Lartigue C (2004). Learning from
Education to Communicate Science as a Good Story.
Endevour. 28: 120-124.
[8] PerinbanayagamR (1991). Discursive Acts. Hawthorne,
NY: Aldine. Polkinhorne DE (1988). Narrative
knowing and the human sciences.
[9] Negrete A (2005). Facts via fiction stories that
communicate science. In Sannit N (Ed.). Motivating
Science.Science communication from a philosophical,
educational and cultural perspective. Luthon pp. 95-
102.
[10] Avraamidou, L.; Osborne, J. (2008) Science as
Narrative: The story of the discovery of penicillin. The
pantaneto forum home page.
www.pantaneto.co.uk/issue31/ retrieved on
18september 2010.
[11] Narrative Science, http://www.narrativescience.com
(accessed in August 2012).
[12] Nichols, N. Machine-Generated Content: Creating
Compelling New Content from Existing Online.
[13] Sources. PhD thesis, Northwestern University,
June2010.GoogleAnalytics,http://www.google.com/ana
lytics(accessed in August 2012)
Author Profile
Ramandeep Ghuman received the B. Tech degree in
Computer Science Engineering from Punjabi
university, Patiala in 2011 and Pursuing M. Tech
degree in Computer Science Engineering from Swami
Vivekanand Engineering Institute of Technology.
Rimpi Kumara worked as Assistant Professor in
Swami Vivekanand Institute of Engineering and
Technology. She received her B. Tech degree in
computer science from GNDU, Amritsar in 2009 and
M. Tech degree in 2011 from GNDU, Amritsar.
Paper ID: 12013162 207